Software Research Associates (Japan)

The effective processing of API calls by cloud providers such as AWS, Microsoft Azure, or GCP has become increasingly challenging as multi-cloud adoption has increased. Reliability, cost maintenance, latency reduction, and performance improvement are some advantages of using several clouds. Traditional approaches frequently fail because they don't respond well to the dynamic and shifting nature of workloads in multi-cloud environment. We solve this by introducing a deep Q-network-based AI approach for autonomous API request routing. This provides reinforcement learning for picking the most relevant and valuable cloud provider based on live metric parameters such as latency, cost, and failure rates. The system remains versatile, subject to changing conditions, to ensure that performance optimization is achieved across AWS, Azure, and GCP. These findings show significant improvements and accomplishments in key performance indicators (KPI). API latency has been reduced from 250 ms to 140 ms, approximately a 44% reduction, with a corresponding decrease in the cost per request of 20%. The success rate has increased from 92% to 98%, and the failure rate has decreased from 8% to 2%. There is also a balanced load across the three clouds <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(34 \%, 33 \%$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$33 \%)$</tex> with an energy savings of 10%. Thus, our results demonstrate that AI-enhanced reinforcement learning can be highly beneficial in enhancing multi-cloud API orchestration. Besides these, they will also be resource-efficient and therefore very scalable solutions for the modern-day cloud architectures.

The increasing demand for real-time processing of data has accelerated rapid growth in edge analytics as well as cloud analytics.Edge analytics refers to performing analytics near the source, which is typically at a device level or local networks, whereas analytics on the cloud would need central cloud infrastructure for analyzing data.This article weighs both these two strategies against performance, scalability, cost, and complexity.Edge analytics operates on lower latency and reduced dependency on bandwidth.It is suitable for applications such as IoT and autonomous systems, where real-time output is critical.However, in some cases, the computational power of edge analytics will be limited, and scaling is difficult.In contrast, cloud analytics provides considerable processing capability and easier integration with large-scale data but may have higher latency and dependency on internet connectivity.This paper reviews these factors and provides insights on how to select the best solution for any given use cases and organizational needs; thus, helping businesses handle real-time data processing challenges.

This paper presents a comprehensive analysis of the migration from Kubernetes Ingress to the emerging Gateway API for traffic management in cloud-native environments. While Kubernetes Ingress has served as a foundational component for exposing services, its limitations in managing complex, multi-tenant, and highly dynamic microservices architectures have become increasingly apparent. The Kubernetes Gateway API, designed with a role-oriented, portable, expressive, and extensible model, offers a significant evolution by decoupling infrastructure and application concerns, providing richer routing capabilities, and enhancing role-based access control. This study details the motivations behind adopting the Gateway API, outlines a practical migration methodology, including architectural comparisons and the utility of tools like ingress2gateway, and discusses the challenges encountered. The observed improvements in operational clarity, routing flexibility, scalability, and security post-migration position the Gateway API as a future-forward standard for robust and maintainable Kubernetes traffic control

Major disasters can often destroy communication networks. To address this issue, companies such as NTT have developed movable base stations (MBSs), which can be easily deployed to construct an emergency communication network. A sample of how MBSs can be connected to form a network can be seen in Fig. 1. However, these networks have performance drawbacks. In order to improve their performance, we propose an algorithm that determines the best way for the MBSs to be connected. The proposed model and algorithm resulted in a reduction in the overall delay and average delay of the system compared to the greedy algorithm. For the realistic constraints, the genetic algorithm (GA) was so successful that its worst-case delay almost matched the average delay of the greedy algorithm. The simulations did show that for extremely high communication rates or extremely slow processing rates, the impact of the topology on the system delay was minimal, and thus both algorithms had similar results. This is because the system bottleneck in these cases is the processing times. However, for the realistic network constraints, the GA outperforms the greedy algorithm by approximately 40% in the fog system and 90% in the cloud system.

This paper presents a new food recording tool, FoodLog Athl, for the healthcare or physical enhancement of its users. Unlike existing food recording tools, we designed the system for dietitians or third parties who monitor the users. The tool not only supports the users by functions such as food image recognition, but also it helps the dietitians watch and communicate with users. Furthermore, it calculates nutritional values from food records - the use of the tool reduces the workload of dietitians and focuses their work on nutrition guidance.

&amp;lt;p&amp;gt;Funding organisations and those that support research software came together to explore how to effectively support both new and existing research software during the &amp;lt;a href="https://www.future-of-research-software.org/"&amp;gt;International Funders Workshop: The Future of Research Software&amp;lt;/a&amp;gt;. The crucial role of software in research is becoming increasingly apparent, as is the urgent need to sustain it and to invest in the people who develop and maintain it. Increased focus on research software will accelerate innovation, reduce information-sharing gaps, encourage innovation, and promote reproducibility, amongst other benefits.&amp;lt;/p&amp;gt;\n\n&amp;lt;p&amp;gt;Read the report below or the &amp;lt;a href="https://www.researchsoft.org/blog/2022-11/"&amp;gt;short blog post&amp;lt;/a&amp;gt; from the Research Software Alliance.&amp;lt;/p&amp;gt;

Diet is a very important factor in people's health management. Applications that record photos of meals and help people manage their diets are used by many users every day. In many cases, such applications use images just to estimate meals and calories. We propose a further use of diet images. The new idea is to read changes in mental health, such as stress and well-being, from the diet image over some period of time. If the applications can recognize the signs of mental health changes that dietary records give, they will be more useful as health management applications. The two contributions of this paper are that we have created a dataset consisting of 24,644 meal items dietary record and mental health records for the same time period(over a 3-month period), and that we have shown that changes in mental health are correlated with changes in diet, and especially, the correlation is stronger in groups with greater change. The potential and goal of this study are to extract features from the images of meals related to mental health.

Model checking is a formal verification technique that automatically decides whether a software system conforms to its specification or not. One of the important features of model checking is to output a counterexample, if a violative execution is detected. Traditional exhaustive techniques fail to complete the verification in large-scale systems because of the shortage of computational resources. This is an efficiency problem called State Explosion Problem. On the other hand, the user of model checking hopes to obtain comprehensible and short outputs. However, balancing efficiency and comprehensibility is not easy, since the exhaustive investigation of the system executions is necessary to find a short counterexample. In this paper, we tackle the balancing problem with an approach of Search-Based Software Engineering. We propose a novel verification algorithm using multi-swarm Particle Swarm Optimization. Our technical breakthrough is to use the specification to decompose the verification problem into the small-sized subproblems dynamically. Each swarm solves one of the subproblems efficiently and the aggregation of the partial solutions found by the swarms provides us with sufficiently short counterexamples. Our numerical experiments show that the proposed algorithm outperforms traditional exhaustive techniques and state-of-the-art non-exhaustive techniques in terms of both efficiency and comprehensibility.

Image courtesy of icon king1 from freeicons.io. Posted on 24 February 2022. Overview of research software funding landscape &lt;em&gt; February 24, 2022 &lt;/em&gt; By Michelle Barker and Daniel S. Katz DOI: https://www.doi.org/10.5281/zenodo.6102487 This report aims to provide a brief overview of the funding landscape for research software throughout its life cycle.

MatCap is an expressive approach to shade 3D models and is promising for the production of typical Japanese-style cel-shaded animations. However, we experienced an asset management problem in our previous short film production; we needed to manually create many MatCap assets to achieve variations shot by shot or even frame by frame. In this work, we identify requirements for shading systems in Japanese animation production and describe our procedural MatCap system, which satisfies the requirements. Procedural MatCap generates customizable MatCap assets fully procedurally in run time, which drastically improves the asset manageability.